Utilizing a machine learning model to identify unhealthy online user behavior and to cause healthy physical user behavior

ABSTRACT

A device receives, from a client device, behavior data indicating an action of a user of the client device, and processes the behavior data, with a model, to determine whether the action satisfies a behavior threshold. The device determines preventative actions to perform to prevent the action of the user, when the action is determined to satisfy the behavior threshold, and performs the preventative actions to prevent the action of the user. The device provides, to the client device, a request indicating that the user perform a physical activity before the one or more preventative actions are disabled, and monitors a performance of the physical activity by the user. The device determines whether the user satisfies the performance of the physical activity based on the monitoring, and disables the one or more preventative actions when it is determined that the user satisfies the performance of the physical activity.

BACKGROUND

Research has shown that excessive use of technology, such as television,video games, and the Internet, creates a variety of mental and medicalhealth issues for individuals. For example, if an individual excessivelyutilizes devices (e.g., computers, tablets, smart phones, televisions,etc.) to browse the Internet, perform online shopping, watch television,and/or the like, the physical health of the individual deteriorates dueto the sedentary nature of such activities. Furthermore, an individualmay suffer mental health issues due to technology, such as onlineshopping addictions, online gambling addictions, social mediaaddictions, and/or the like.

SUMMARY

According to some implementations, a method may include receiving, froma client device, behavior data indicating an action of a user of theclient device, wherein the action may be performed by the user via theclient device, and wherein the action may be associated with onlineactivity of the user via the client device. The method may includeprocessing the behavior data, with a machine learning model, todetermine whether the action satisfies a behavior threshold, wherein thebehavior threshold may be associated with an online usage time of theuser via the client device or a usage of an online resource by the uservia the client device. The method may include determining one or morepreventative actions to perform to mitigate the action of the user,wherein the one or more preventative actions may be determined based onthe action and when the action is determined to satisfy the behaviorthreshold. The method may include performing the one or morepreventative actions to mitigate the action of the user, wherein the oneor more preventative actions may relate to blocking or disabling one ormore functions of the client device. The method may include providing,to the client device, a request indicating that the user perform aphysical activity before the one or more preventative actions aredisabled, and monitoring a performance of the physical activity by theuser. The method may include determining whether the user satisfies theperformance of the physical activity based on monitoring the performanceof the physical activity by the user, and selectively maintaining ordisabling the one or more preventative actions based on whether the usersatisfies the performance of the physical activity. The one or morepreventative actions may be maintained when the user fails to satisfythe performance of the physical activity, and the one or morepreventative actions may be disabled when the user satisfies theperformance of the physical activity.

According to some implementations, a device may include one or morememories, and one or more processors, communicatively coupled to the oneor more memories, to receive a machine learning model that has beentrained to determine whether an action of a user satisfies a behaviorthreshold, wherein the behavior threshold may be associated with anonline usage time of the user via a client device, or a usage of anonline resource by the user via the client device. The one or moreprocessors may receive, from the client device, behavior data indicatingthe action of the user, wherein the action may be performed by the uservia the client device, and may process the behavior data, with themachine learning model, to determine whether the action satisfies thebehavior threshold. The one or more processors may determine apreventative action to perform to prevent the action of the user,wherein the preventative action may be determined based on the actionand when the action is determined to satisfy the behavior threshold. Theone or more processors may perform the preventative action to preventthe action of the user, wherein the preventative action may relate toblocking or disabling one or more functions of the client device. Theone or more processors may provide, to the client device, a requestindicating that the user perform a physical activity before thepreventative action is disabled, and may monitor a performance of thephysical activity by the user. The one or more processors may determinewhether the user satisfies the performance of the physical activitybased on monitoring the performance of the physical activity by theuser, and may maintain the preventative action when the user fails tosatisfy the performance of the physical activity.

According to some implementations, a non-transitory computer-readablemedium may store instructions that include one or more instructionsthat, when executed by one or more processors of a device, cause the oneor more processors to receive, from a client device, behavior dataindicating an action of a user of the client device, wherein the actionmay be performed by the user via the client device. The one or moreinstructions may cause the one or more processors to process thebehavior data, with a machine learning model, to determine whether theaction satisfies a behavior threshold, wherein the behavior thresholdmay be associated with an online usage time of the user via the clientdevice, or a usage of an online resource by the user via the clientdevice. The one or more instructions may cause the one or moreprocessors to determine one or more preventative actions to perform toprevent the action of the user, wherein the one or more preventativeactions may be determined based on the action and when the action isdetermined to satisfy the behavior threshold. The one or moreinstructions may cause the one or more processors to perform the one ormore preventative actions to prevent the action of the user, wherein theone or more preventative actions may relate to blocking or disabling oneor more functions of the client device. The one or more instructions maycause the one or more processors to provide, to the client device, arequest indicating that the user perform a physical activity before theone or more preventative actions are disabled, and monitor a performanceof the physical activity by the user. The one or more instructions maycause the one or more processors to determine whether the user satisfiesthe performance of the physical activity based on monitoring theperformance of the physical activity by the user, and disable the one ormore preventative actions when it is determined that the user satisfiesthe performance of the physical activity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1H are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for utilizing a machinelearning model to identify unhealthy online user behavior and to causehealthy physical user behavior.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

There are several costs associated with the mental and physical healthissues created by excessive use of technology. For example, the mentaland physical health issues waste computing resources (e.g., processingresources, memory resources, and/or the like), network resources, andhuman resources associated with treating mentally unhealthy individuals,treating physically unhealthy individuals, and/or the like, at doctor'soffices, pharmacies, hospitals, and/or the like. Furthermore, the mentaland physical health issues decrease productivity of individuals at work,which causes delays of projects and schedules at work (e.g., associatedwith a production of a good, a provision of a service, and/or the like).Such work delays cause computing resources and network resources to sitidle and be wasted while waiting for the delays. Finally, the mental andphysical health issues require businesses to replace individuals toounhealthy to work and/or hire additional individuals to make up for lostproductivity, which wastes computing resources and network resourcesassociated with locating and hiring the replacement and/or additionalindividuals, as well as training those replacement and/or additionalindividuals.

Some implementations described herein provide a health platform thatutilizes a machine learning model to identify unhealthy online userbehavior and to cause healthy physical user behavior. For example, thehealth platform may receive, from a client device, behavior dataindicating an action of a user of the client device, wherein the actionmay be performed by the user via the client device, and wherein theaction may be associated with online activity of the user via the clientdevice. The health platform may process the behavior data, with amachine learning model, to determine whether the action satisfies abehavior threshold, wherein the behavior threshold may be associatedwith an online usage time of the user via the client device or a usageof an online resource by the user via the client device. The healthplatform may determine preventative actions to perform to mitigate theaction of the user, wherein the preventative actions may be determinedbased on the action and when the action is determined to satisfy thebehavior threshold. The health platform may perform the preventativeactions to mitigate the action of the user, wherein the preventativeactions may relate to blocking or disabling one or more functions of theclient device. The health platform may provide, to the client device, arequest indicating that the user perform a physical activity before thepreventative actions are disabled, and may monitor a performance of thephysical activity by the user. The health platform may determine whetherthe user satisfies the performance of the physical activity based onmonitoring the performance of the physical activity by the user, and mayselectively maintain or disable the preventative actions based onwhether the user satisfies the performance of the physical activity. Thepreventative actions may be maintained when the user fails to satisfythe performance of the physical activity, and the preventative actionsmay be disabled when the user satisfies the performance of the physicalactivity.

In this way, the health platform conserves computing resources andnetwork resources that would otherwise be wasted treating mentallyunhealthy individuals, treating physically unhealthy individuals, and/orthe like, at doctor's offices, pharmacies, hospitals, and/or the like.Furthermore, the health platform conserves computing resources andnetwork resources that would otherwise be wasted addressing delays ofprojects and schedules at work caused by mental and physical healthissues of employees. Finally, the health platform conserves computingresources and network resources that would otherwise be wasted locatingand hiring replacement and/or additional individuals for mentally and/orphysically unhealthy employees.

FIGS. 1A-1H are diagrams of one or more example implementations 100described herein. As shown in FIG. 1A, a client device (e.g., astationary client device, such as desktop computer) may be associatedwith a health platform and a user. The user may cause the client deviceto request a health application from the health platform. As furthershown, and by reference number 105, the health platform may provide thehealth application to the client device. In some implementations, thehealth application may enable the client device to perform functionsdescribed herein as being performed by the client device. In someimplementations, and as further shown in FIG. 1A, the client device mayassociate other client devices (e.g., utilized by and associated withthe user) with the health application, such as a mobile client device(e.g., a smart phone that includes one or more sensors), a wearableclient device (e.g., a smart watch that includes one or more sensors), atelevision, and/or the like. In some implementations, the healthplatform may be associated with hundreds, thousands, millions, and/orthe like of client devices and users and may provide the healthapplication to the client devices.

As shown in FIG. 1B, and by reference number 110, the health platformmay receive, from the client device or from one of the associated clientdevices, behavior data indicating an action of the user via the clientdevice or one of the associated client devices. In some implementations,the action may include an action that may be detrimental to a mentalhealth, a physical health, a financial health, and/or the like of theuser. For example, the action may include browsing the Internet,shopping online, playing a game, viewing inappropriate or indecentcontent, and/or the like for a period of time greater than a thresholdquantity of time (e.g., minutes, one hour, two hours, and/or the like).Such actions may cause the user to be sedentary for extended periods oftime (e.g., which may deteriorate the physical health of the user). Insome implementations, the action may be determined based on data fromone or more sensors associated with the client device and/or one or moreof the associated client devices. Thus, the sensors may capture sensordata of the user, such as a heart rate of the user, a location of theuser, and/or the like, and may provide the sensor data to the healthapplication. The health application may determine behavior data based onthe sensor data, and may provide the behavior data to the healthplatform periodically (e.g., every minute, every 5 minutes, etc.).

In another example, the action may include spending a quantity of money(e.g., greater than a threshold quantity) on in-game purchases, makingonline shopping purchases, making online gambling bets, and/or the like.Such actions may cause the user to be sedentary for extended periods oftime (e.g., which may deteriorate the physical health of the user); maycause the user to suffer financial losses (e.g., which may bedetrimental to the financial health of the user); may enable a gamingaddiction, an online shopping addition, an online gambling addiction,and/or a social media addiction of the user (e.g., which may bedetrimental to the mental health of the user); and/or the like.

As shown in FIG. 1C, and by reference number 115, the health platformmay process the behavior data, with a machine learning model, todetermine whether the action satisfies a behavior threshold. In someimplementations, the machine learning model may include a patternrecognition model that generates predictions indicating whether anaction satisfies a behavior threshold. In some implementations, thebehavior threshold may include a threshold quantity of time (e.g., onehour, two hours, and/or the like) when the action includes browsing theInternet, shopping online, playing a game, etc.; a threshold quantity ofmoney when the action includes spending money on in-game purchases,making online shopping purchases, making online gambling bets, etc.;and/or the like. In some implementations, the machine learning model maydetermine whether a web site matches features of a type of web site(e.g., an online gambling web site, an online shopping web site, anonline gaming web site, and/or the like) when determining whether theaction satisfies the behavior threshold. Such implementations may aidthe health platform in assessing an action associated with an unknownweb site.

In some implementations, the health platform may perform a trainingoperation on the machine learning model with the historical behaviordata. The historical behavior data may include behavior data indicatingthat users browsed the Internet, shopped online, played games, etc. fora period of time greater than or less than the threshold quantity oftime; behavior data indicating that users spent quantities of money(e.g., greater than or less than the threshold quantity of money) onin-game purchases, making online shopping purchases, making onlinegambling bets, etc.; data indicating features associated with onlineshopping web sites, online gambling web sites, online gaming web sites,etc.; behavior data indicating that users interacted with fields withinweb pages; behavior data indicating that new content was rendered on webpages (e.g., which indicates that users are navigating and selectinginformation provided by web pages); behavior data indicating monitorednetwork traffic associated with the users; and/or the like.

In some implementations, the health platform may separate the historicalbehavior data into a training set, a validation set, a test set, and/orthe like. The training set may be utilized to train the machine learningmodel. The validation set may be utilized to validate results generatedbased on training the machine learning model with the training set. Thetest set may be utilized to test results generated by the trainedmachine learning model.

In some implementations, the health platform may train the machinelearning model using, for example, an unsupervised training procedureand based on the training set of the historical behavior data. Forexample, the health platform may perform dimensionality reduction toreduce the historical behavior data to a minimum feature set, therebyreducing resources (e.g., processing resources, memory resources, and/orthe like) to train the machine learning model and may apply aclassification technique to the minimum feature set.

In some implementations, the health platform may use a logisticregression classification technique to determine a categorical outcome(e.g., that actions satisfy or fail to satisfy the behavior threshold).Additionally, or alternatively, the health platform may use a naïveBayesian classifier technique. In this case, the health platform mayperform binary recursive partitioning to split the historical behaviordata into partitions and/or branches and use the partitions and/orbranches to perform predictions (e.g., that actions satisfy or fail tosatisfy the behavior threshold). Based on using recursive partitioning,the health platform may reduce utilization of computing resourcesrelative to manual, linear sorting and analysis of data points, therebyenabling use of thousands, millions, or billions of data points to trainthe machine learning model, which may result in a more accurate modelthan using fewer data points.

Additionally, or alternatively, the health platform may use a supportvector machine (SVM) classifier technique to generate a non-linearboundary between data points in the training set. In this case, thenon-linear boundary is used to classify test data into a particularclass.

Additionally, or alternatively, the health platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the machine learning model from a subjectmatter expert, which may reduce an amount of time, an amount ofprocessing resources, and/or the like to train the machine learningmodel relative to an unsupervised training procedure. In someimplementations, the health platform may use one or more other modeltraining techniques, such as a neural network technique, a latentsemantic indexing technique, and/or the like. For example, the healthplatform may perform an artificial neural network processing technique(e.g., using a two-layer feedforward neural network architecture, athree-layer feedforward neural network architecture, and/or the like) toperform pattern recognition with regard to optimal regions of thehistorical behavior data. In this case, using the artificial neuralnetwork processing technique may improve an accuracy of the trainedmachine learning model generated by the health platform by enabling themodel to be more robust than unprocessed models to noisy, imprecise, orincomplete data, and by enabling the health platform to detect patternsand/or trends undetectable to human analysts or systems using lesscomplex techniques.

In some implementations, the health platform may receive the trainedmachine learning model from another source. In such implementations, thehealth platform may utilize the trained machine learning model toprocess the behavior data and to determine whether the action satisfiesthe behavior threshold.

In this way, the health platform may provide the behavior data (e.g.,indicating the action of the user) as an input to the machine learningmodel, and the machine learning model may output information indicatingwhether the action of the user satisfies the behavior threshold based onthe input.

As shown in FIG. 1D, and by reference number 120, the health platformmay determine one or more preventative actions to perform to mitigatethe action. In some implementations, the health platform may determinethe one or more preventative actions based on the action and when thehealth platform determines that the action satisfies the behaviorthreshold. If the health platform determines that the action fails tosatisfy the behavior threshold, the health platform may omit determiningthe one or more preventative actions until the action satisfies thebehavior threshold. The one or more preventative actions are describedbelow in connection with FIG. 1E.

As shown in FIG. 1E, and by reference number 125, the health platformmay perform the one or more preventative actions. In someimplementations, the one or more preventative actions may be directed tothe client device and/or the associated client devices. For example, theone or more preventative actions may include the health platform causingthe client device and/or the associated client devices to disable abrowser associated with the client device for a particular period oftime. In this way, the health platform may prevent the user fromutilizing the browser to perform the action to be prevented (e.g.,browsing the Internet, online gaming, online shopping, online gambling,viewing inappropriate content, and/or the like), which may improve thehealth (e.g., the mental health, the physical health, the financialhealth, and/or the like) of the user and conserve client devicecomputing resources and network resources.

In some implementations, the one or more preventative actions mayinclude the health platform causing the client device and/or theassociated client devices to block a display of a browser windowprovided by the client device. In this way, the health platform mayprevent the user from performing functions associated with the browserwindow (e.g., viewing inappropriate web sites, utilizing online gamblingweb sites, utilizing online shopping web sites, and/or the like), whichmay improve the health of the user and conserve client device computingresources and network resources.

In some implementations, the one or more preventative actions mayinclude the health platform causing the client device and/or theassociated client devices to block a display of a particular web site(e.g., an online shopping web site, an online gambling web site, anindecent web site, and/or the like). In this way, the health platformmay prevent the user from accessing the particular web site (e.g., anonline shopping web site, an online gambling web site, and/or the like),which may improve the health of the user and conserve client devicecomputing resources and network resources.

In some implementations, the one or more preventative actions mayinclude the health platform causing the client device and/or theassociated client devices to be disabled. In this way, the healthplatform may prevent the user from utilizing electronic devices for aperiod of time, which may improve the health of the user and conserveclient device computing resources and network resources.

In some implementations, the one or more preventative actions mayinclude the health platform causing the client device and/or theassociated client devices to remove a tab from a browser window. In thisway, the health platform may prevent the user from accessing a web siteassociated with the removed tab, which may improve the health of theuser and conserve client device computing resources and networkresources.

In some implementations, the one or more preventative actions mayinclude the health platform causing the client device and/or theassociated client devices to block a display of an application (e.g., agaming application). In this way, the health platform may prevent theuser from overutilizing the application, which may improve the health ofthe user and conserve client device computing resources and networkresources.

In some implementations, the one or more preventative actions mayinclude the health platform causing the client device and/or theassociated client devices to block a display of a desktop. In this way,the health platform may prevent the user from overutilizing the clientdevice and/or the associated client devices, which may improve thehealth of the user and conserve client device computing resources andnetwork resources.

The above preventative actions are provided simply by way of example.The health platform may cause other preventative actions to beperformed, in addition to or alternatively to the preventative actionsdescribed above. For example, the health platform may cause the clientdevice to display a message, instructing the user to stand up and movearound for a particular period of time, such as perform one or moreexercises or go for a walk. In some implementations, the health platformmay cause a web page (e.g., one directed to improving mental or physicalhealth) to be displayed via the client device and/or one or more of theadditional client devices.

In some implementations, additional sensor data from the client deviceand/or the additional client devices may indicate that the user hasignored instructions to stand up and move around. In theseimplementations, the health platform may cause a message to be providedor a telephone call to be placed to a user device associated withanother user. For example, in the situation where the user is a child,the health platform may provide a message or place a telephone call to aparent of the child to notify the parent of the detected activity.

In some implementations, the preventative action may include disabling atransaction card associated with the user. For example, when thebehavior relates to gambling or excessive online purchases, the healthplatform may cause further charges (e.g., to a gambling website oronline store) to be blocked.

As shown in FIG. 1F, and by reference number 130, the health platformmay provide, to the client device, a request indicating that the userperform a physical activity before the one or more preventative actionsare disabled. In some implementations, the physical activity may includethe user ceasing the action for a time period, the user performing aparticular physical activity (e.g., jogging, running, walking, jumpingjacks, lifting weights, etc.) for a time period, the user performing aparticular physical activity until a particular heart rate of the useris achieved, the user giving the client device to another user (e.g., aparent, a wife, a husband, etc.), the user giving a transaction card tothe other user, and/or the like. The client device may receive therequest from the health platform and may display the request via a userinterface. For example, as shown in FIG. 1F, the client device maydisplay a user interface (e.g., blocking a desktop display of the clientdevice) indicating that the “action will be blocked until you exercisefor X minutes and achieve a heart rate of Y beats per minute.”

As shown in FIG. 1G, and by reference number 135, the health platformmay monitor a performance of the physical activity of the user. In someimplementations, the health platform may monitor the performance of thephysical activity of the user by monitoring video received from a cameraassociated with the client device (e.g., showing the user performing thephysical activity and utilizing computer vision to detect movement ofthe user), monitoring a wearable device worn by the user (e.g., showinga heart rate of the user, which may indicate that the user is performingthe physical activity, such as exercising), monitoring a time periodassociated with how long the user is to perform the physical activity,monitoring notifications received from other users (e.g., indicatingthat the user is performing the physical activity or has performed thephysical activity), monitoring interactions with the client device(e.g., filling in fields, selecting items, etc.), and/or the like. Insome implementations, the health platform may monitor the physicalactivity of the user (e.g., when there is not physical activity) toensure that it was appropriate to perform the one or more preventativeactions (e.g., it would not be appropriate to disable a display of theclient device if the user is not utilizing the display).

As further shown in FIG. 1G, and by reference number 140, the healthplatform may determine whether the user satisfies the performance of thephysical activity. In some implementations, the health platform maydetermine whether the user satisfies the performance of the physicalactivity by determining whether the camera associated with the clientdevice shows the user performing the physical activity (e.g.,exercising), determining whether the wearable device worn by the userindicates that the user is performing the physical activity and achieveda particular heart rate, determining whether the time period associatedwith how long the user is to perform the physical activity has expired,determining whether notifications are received from the other usersindicating that the user is performing the physical activity or hasperformed the physical activity, determining whether the user isinteracting with the client device (e.g., filling in fields, selectingitems, etc.), and/or the like. In some implementations, if the healthplatform determines that the user fails to satisfy the performance ofthe physical activity, the health platform may maintain the one or morepreventative actions.

In some implementations, if the health platform determines that the usersatisfies the performance of the physical activity, the health platformmay disable the one or more preventative actions. As shown in FIG. 1H,and by reference number 145, the health platform may disable the one ormore preventative actions based on determining that the user satisfiesthe performance of the physical activity. For example, based ondetermining that the user satisfies the performance of the physicalactivity, the health platform may cause the client device to remove theuser interface blocking the desktop display of the client device.Therefore, the client device may provide the desktop display to theuser.

In this way, several different stages of the process for identifyingunhealthy online user behavior and causing healthy physical userbehavior may be determined using a machine learning model, which mayconserve computing resources (e.g., processing resources, memoryresources, and/or the like). For example, disabling a client device whenunhealthy behavior is detected conserves computing resources.Furthermore, implementations described herein use a rigorous,computerized process to perform tasks or roles that were not previouslyperformed. For example, currently there does not exist a technique thatutilizes a machine learning model to identify unhealthy online userbehavior and to cause healthy physical user behavior. Further,automating the process for identifying unhealthy online user behaviorand causing healthy physical user behavior conserves computing resources(e.g., processing resources, memory resources, and/or the like) thatwould otherwise be wasted in addressing mental health issues, physicalhealth issues, and/or financial health issues of users.

As indicated above, FIGS. 1A-1H are provided merely as examples. Otherexamples may differ from what is described with regard to FIGS. 1A-1H.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a client device 210, a health platform220, and a network 230. Devices of environment 200 may interconnect viawired connections, wireless connections, or a combination of wired andwireless connections.

Client device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, client device 210 may includea mobile phone (e.g., a smart phone, a radiotelephone, etc.), a laptopcomputer, a tablet computer, a desktop computer, a handheld computer, agaming device, a wearable communication device (e.g., a smartwristwatch, a pair of smart eyeglasses, etc.), a television, or asimilar type of device. In some implementations, client device 210 mayreceive information from and/or transmit information to health platform220. In some implementations, client device 210 may be associated withone or more sensors. The one or more sensors may include, for example, acamera, a heart rate monitor, a motion sensor, a location sensor (e.g.,a GPS sensor), and/or any other type of sensor that would aid in theidentification of unhealthy physical and/or mental behavior.

Health platform 220 includes one or more devices that may utilize amachine learning model to identify unhealthy online user behavior and tocause healthy physical user behavior. In some implementations, healthplatform 220 may be modular such that certain software components may beswapped in or out depending on a particular need. As such, healthplatform 220 may be easily and/or quickly reconfigured for differentuses. In some implementations, health platform 220 may receiveinformation from and/or transmit information to one or more clientdevices 210.

In some implementations, as shown, health platform 220 may be hosted ina cloud computing environment 222. Notably, while implementationsdescribed herein describe health platform 220 as being hosted in cloudcomputing environment 222, in some implementations, health platform 220may be non-cloud-based (i.e., may be implemented outside of a cloudcomputing environment) or may be partially cloud-based.

Cloud computing environment 222 includes an environment that may hosthealth platform 220. Cloud computing environment 222 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that host health platform 220. As shown,cloud computing environment 222 may include a group of computingresources 224 (referred to collectively as “computing resources 224” andindividually as “computing resource 224”).

Computing resource 224 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 224 may host health platform 220. The cloud resources mayinclude compute instances executing in computing resource 224, storagedevices provided in computing resource 224, data transfer devicesprovided by computing resource 224, etc. In some implementations,computing resource 224 may communicate with other computing resources224 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2, computing resource 224 includes a group ofcloud resources, such as one or more applications (“APPs”) 224-1, one ormore virtual machines (“VMs”) 224-2, virtualized storage (“VSs”) 224-3,one or more hypervisors (“HYPs”) 224-4, and/or the like.

Application 224-1 includes one or more software applications that may beprovided to or accessed by client device 210. Application 224-1 mayeliminate a need to install and execute the software applications onclient device 210. For example, application 224-1 may include softwareassociated with health platform 220 and/or any other software capable ofbeing provided via cloud computing environment 222. In someimplementations, one application 224-1 may send/receive informationto/from one or more other applications 224-1, via virtual machine 224-2.

Virtual machine 224-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 224-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 224-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 224-2 may execute on behalf of a user(e.g., a user of client device 210 or an operator of health platform220), and may manage infrastructure of cloud computing environment 222,such as data management, synchronization, or long-duration datatransfers.

Virtualized storage 224-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 224. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may provide administrators ofthe storage system with flexibility in how the administrators managestorage for end users. File virtualization may eliminate dependenciesbetween data accessed at a file level and a location where files arephysically stored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 224-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 224.Hypervisor 224-4 may present a virtual operating platform to the guestoperating systems and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 230 includes one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)),a private network, an ad hoc network, an intranet, the Internet, a fiberoptic-based network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device and/or a single device shown in FIG.2 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to client device 210, health platform 220, and/orcomputing resource 224. In some implementations, client device 210,health platform 220, and/or computing resource 224 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3, device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and/or a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random-access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid-state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface,and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for utilizing a machinelearning model to identify unhealthy online user behavior and to causehealthy physical user behavior. In some implementations, one or moreprocess blocks of FIG. 4 may be performed by a health platform (e.g.,health platform 220). In some implementations, one or more processblocks of FIG. 4 may be performed by another device or a group ofdevices separate from or including the health platform, such as a clientdevice (e.g., client device 210).

As shown in FIG. 4, process 400 may include receiving, from a clientdevice, behavior data indicating an action of a user of the clientdevice, wherein the action is performed by the user via the clientdevice, and wherein the action is associated with online activity of theuser via the client device (block 410). For example, the health platform(e.g., using computing resource 224, processor 320, communicationinterface 370, and/or the like) may receive, from a client device,behavior data indicating an action of a user of the client device, asdescribed above in connection with FIGS. 1A-2. In some implementations,the action is performed by the user via the client device. In someimplementations, the action is associated with online activity of theuser via the client device.

As further shown in FIG. 4, process 400 may include processing thebehavior data, with a machine learning model, to determine whether theaction satisfies a behavior threshold, wherein the behavior threshold isassociated with an online usage time of the user via the client deviceor a usage of an online resource by the user via the client device(block 420). For example, the health platform (e.g., using computingresource 224, processor 320, memory 330, and/or the like) may processthe behavior data, with a machine learning model, to determine whetherthe action satisfies a behavior threshold, as described above inconnection with FIGS. 1A-2. In some implementations, the behaviorthreshold may be associated with an online usage time of the user viathe client device or a usage of an online resource by the user via theclient device.

As further shown in FIG. 4, process 400 may include determining one ormore preventative actions to perform to mitigate the action of the user,wherein the one or more preventative actions are determined based on theaction and when the action is determined to satisfy the behaviorthreshold (block 430). For example, the health platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may determine one or more preventative actions to perform tomitigate the action of the user, as described above in connection withFIGS. 1A-2. In some implementations, the one or more preventativeactions may be determined based on the action and when the action isdetermined to satisfy the behavior threshold.

As further shown in FIG. 4, process 400 may include performing the oneor more preventative actions to mitigate the action of the user, whereinthe one or more preventative actions relate to blocking or disabling oneor more functions of the client device (block 440). For example, thehealth platform (e.g., using computing resource 224, processor 320,communication interface 370, and/or the like) may perform the one ormore preventative actions to mitigate the action of the user, asdescribed above in connection with FIGS. 1A-2. In some implementations,the one or more preventative actions may relate to blocking or disablingone or more functions of the client device.

As further shown in FIG. 4, process 400 may include providing, to theclient device, a request indicating that the user perform a physicalactivity before the one or more preventative actions are disabled (block450). For example, the health platform (e.g., using computing resource224, processor 320, communication interface 370, and/or the like) mayprovide, to the client device, a request indicating that the userperform a physical activity before the one or more preventative actionsare disabled, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include monitoring aperformance of the physical activity by the user (block 460). Forexample, the health platform (e.g., using computing resource 224,processor 320, memory 330, communication interface 370, and/or the like)may monitor a performance of the physical activity by the user, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include determining whetherthe user satisfies the performance of the physical activity based onmonitoring the performance of the physical activity by the user (block470). For example, the health platform (e.g., using computing resource224, processor 320, storage component 340, and/or the like) maydetermine whether the user satisfies the performance of the physicalactivity based on monitoring the performance of the physical activity bythe user, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 4, process 400 may include selectivelymaintaining or disabling the one or more preventative actions based onwhether the user satisfies the performance of the physical activity,wherein the one or more preventative actions are maintained when theuser fails to satisfy the performance of the physical activity, andwherein the one or more preventative actions are disabled when the usersatisfies the performance of the physical activity (block 480). Forexample, the health platform (e.g., using computing resource 224,processor 320, memory 330, communication interface 370, and/or the like)may selectively maintain or disable the one or more preventative actionsbased on whether the user satisfies the performance of the physicalactivity, as described above in connection with FIGS. 1A-2. In someimplementations, the one or more preventative actions may be maintainedwhen the user fails to satisfy the performance of the physical activity.In some implementations, the one or more preventative actions may bedisabled when the user satisfies the performance of the physicalactivity.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, when performing the one or more preventativeactions, the health platform may cause the client device to be disabled.In some implementations, when performing the one or more preventativeactions, the health platform may cause the client device to disable abrowser associated with the client device, may cause the client deviceto block a display of a browser window associated with the clientdevice, may cause the client device to block a display of a particularweb site utilized by the user via the client device, may cause theclient device to remove a tab from a browser window associated with theclient device, may cause the client device to block a display of anapplication utilized by the user via the client device, and/or may causethe client device to block a display of a desktop associated with theclient device.

In some implementations, the physical activity may include the userceasing the action for a first time period, the user performing aparticular physical activity for a second period, and/or the userperforming the particular physical activity until a particular heartrate of the user is achieved. In some implementations, when monitoringthe performance of the physical activity by the user, the healthplatform may monitor the performance of the physical activity via acamera associated with the client device, may monitor the performance ofthe physical activity via a wearable device associated with the user,and/or may monitor the performance of the physical activity via userinteractions with the client device.

In some implementations, the health platform may provide, to the clientdevice and prior to receiving the behavior data, an application to beinstalled on and executed by the client device, and, when receiving thebehavior data, the health platform may receive the behavior data via theapplication. In some implementations, the user may be associated withone or more other client devices, and the health platform may performthe one or more preventative actions, on the one or more other clientdevices, to mitigate the action of the user.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for utilizing a machinelearning model to identify unhealthy online user behavior and to causehealthy physical user behavior. In some implementations, one or moreprocess blocks of FIG. 5 may be performed by a health platform (e.g.,health platform 220). In some implementations, one or more processblocks of FIG. 5 may be performed by another device or a group ofdevices separate from or including the health platform, such as a clientdevice (e.g., client device 210).

As shown in FIG. 5, process 500 may include receiving a machine learningmodel that has been trained to determine whether an action of a usersatisfies a behavior threshold, wherein the behavior threshold isassociated with: an online usage time of the user via a client device,or a usage of an online resource by the user via the client device(block 510). For example, the health platform (e.g., using computingresource 224, processor 320, communication interface 370, and/or thelike) may receive a machine learning model that has been trained todetermine whether an action of a user satisfies a behavior threshold, asdescribed above in connection with FIGS. 1A-2. In some implementations,the behavior threshold may be associated with an online usage time ofthe user via a client device, or a usage of an online resource by theuser via the client device.

As further shown in FIG. 5, process 500 may include receiving, from theclient device, behavior data indicating the action of the user, whereinthe action is performed by the user via the client device (block 520).For example, the health platform (e.g., using computing resource 224,processor 320, communication interface 370, and/or the like) mayreceive, from the client device, behavior data indicating the action ofthe user, as described above in connection with FIGS. 1A-2. In someimplementations, the action may be performed by the user via the clientdevice.

As further shown in FIG. 5, process 500 may include processing thebehavior data, with the machine learning model, to determine whether theaction satisfies the behavior threshold (block 530). For example, thehealth platform (e.g., using computing resource 224, processor 320,storage component 340, and/or the like) may process the behavior data,with the machine learning model, to determine whether the actionsatisfies the behavior threshold, as described above in connection withFIGS. 1A-2.

As further shown in FIG. 5, process 500 may include determining apreventative action to perform to prevent the action of the user,wherein the preventative action is determined based on the action andwhen the action is determined to satisfy the behavior threshold (block540). For example, the health platform (e.g., using computing resource224, processor 320, memory 330, and/or the like) may determine apreventative action to perform to prevent the action of the user, asdescribed above in connection with FIGS. 1A-2. In some implementations,the preventative action may be determined based on the action and whenthe action is determined to satisfy the behavior threshold.

As further shown in FIG. 5, process 500 may include performing thepreventative action to prevent the action of the user, wherein thepreventative action relates to blocking or disabling one or morefunctions of the client device (block 550). For example, the healthplatform (e.g., using computing resource 224, processor 320, memory 330,communication interface 370, and/or the like) may perform thepreventative action to prevent the action of the user, as describedabove in connection with FIGS. 1A-2. In some implementations, thepreventative action may relate to blocking or disabling one or morefunctions of the client device.

As further shown in FIG. 5, process 500 may include providing, to theclient device, a request indicating that the user perform a physicalactivity before the preventative action is disabled (block 560). Forexample, the health platform (e.g., using computing resource 224,processor 320, communication interface 370, and/or the like) mayprovide, to the client device, a request indicating that the userperform a physical activity before the preventative action is disabled,as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include monitoring aperformance of the physical activity by the user (block 570). Forexample, the health platform (e.g., using computing resource 224,processor 320, memory 330, communication interface 370, and/or the like)may monitor a performance of the physical activity by the user, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include determining whetherthe user satisfies the performance of the physical activity based onmonitoring the performance of the physical activity by the user (block580). For example, the health platform (e.g., using computing resource224, processor 320, storage component 340, and/or the like) maydetermine whether the user satisfies the performance of the physicalactivity based on monitoring the performance of the physical activity bythe user, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 5, process 500 may include maintaining thepreventative action when the user fails to satisfy the performance ofthe physical activity (block 590). For example, the health platform(e.g., using computing resource 224, processor 320, communicationinterface 370, and/or the like) may maintain the preventative actionwhen the user fails to satisfy the performance of the physical activity,as described above in connection with FIGS. 1A-2.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, the health platform may disable thepreventative action when the user satisfies the performance of thephysical activity. In some implementations, the preventative action mayinclude causing the client device to disable a browser associated withthe client device, causing the client device to block a display of abrowser window associated with the client device, causing the clientdevice to block a display of a particular web site utilized by the uservia the client device, causing the client device to be disabled, causingthe client device to remove a tab from a browser window associated withthe client device, causing the client device to block a display of anapplication utilized by the user via the client device, and/or causingthe client device to block a display of a desktop associated with theclient device.

In some implementations, the action may include the user utilizing theclient device to access and browse the Internet for a time period, theuser utilizing the client device to make online purchases that satisfy aprice threshold, the user utilizing the client device to view anindecent web site, and/or the user utilizing the client device to onlinegamble an amount that satisfies a gambling threshold. In someimplementations, the physical activity may include the user ceasing theaction for a first time period, the user performing a particularphysical activity for a second period, and/or the user performing theparticular physical activity until a heart rate of the user is achieved.

In some implementations, when monitoring the performance of the physicalactivity by the user, the health platform may monitor the performance ofthe physical activity via a camera associated with the client device,may monitor the performance of the physical activity via a wearabledevice associated with the user, and/or may monitor the performance ofthe physical activity via user interactions with the client device. Insome implementations, the health platform may provide, to the clientdevice and prior to receiving the behavior data, an application to beinstalled on and executed by the client device, where the applicationmay cause the client device to provide the behavior data to the device,and enable monitoring the performance of the physical activity by theuser.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for utilizing a machinelearning model to identify unhealthy online user behavior and to causehealthy physical user behavior. In some implementations, one or moreprocess blocks of FIG. 6 may be performed by a health platform (e.g.,health platform 220). In some implementations, one or more processblocks of FIG. 6 may be performed by another device or a group ofdevices separate from or including the health platform, such as a clientdevice (e.g., client device 210).

As shown in FIG. 6, process 600 may include receiving, from a clientdevice, behavior data indicating an action of a user of the clientdevice, wherein the action is performed by the user via the clientdevice (block 610). For example, the health platform (e.g., usingcomputing resource 224, processor 320, communication interface 370,and/or the like) may receive, from a client device, behavior dataindicating an action of a user of the client device, as described abovein connection with FIGS. 1A-2. In some implementations, the action maybe performed by the user via the client device.

As further shown in FIG. 6, process 600 may include processing thebehavior data, with a machine learning model, to determine whether theaction satisfies a behavior threshold, wherein the behavior threshold isassociated with: an online usage time of the user via the client device,or a usage of an online resource by the user via the client device(block 620). For example, the health platform (e.g., using computingresource 224, processor 320, memory 330, and/or the like) may processthe behavior data, with a machine learning model, to determine whetherthe action satisfies a behavior threshold, as described above inconnection with FIGS. 1A-2. In some implementations, the behaviorthreshold may be associated with an online usage time of the user viathe client device, or a usage of an online resource by the user via theclient device.

As further shown in FIG. 6, process 600 may include determining one ormore preventative actions to perform to prevent the action of the user,wherein the one or more preventative actions are determined based on theaction and when the action is determined to satisfy the behaviorthreshold (block 630). For example, the health platform (e.g., usingcomputing resource 224, processor 320, storage component 340, and/or thelike) may determine one or more preventative actions to perform toprevent the action of the user, as described above in connection withFIGS. 1A-2. In some implementations, the one or more preventativeactions may be determined based on the action and when the action isdetermined to satisfy the behavior threshold.

As further shown in FIG. 6, process 600 may include performing the oneor more preventative actions to prevent the action of the user, whereinthe one or more preventative actions relate to blocking or disabling oneor more functions of the client device (block 640). For example, thehealth platform (e.g., using computing resource 224, processor 320,communication interface 370, and/or the like) may perform the one ormore preventative actions to prevent the action of the user, asdescribed above in connection with FIGS. 1A-2. In some implementations,the one or more preventative actions may relate to blocking or disablingone or more functions of the client device.

As further shown in FIG. 6, process 600 may include providing, to theclient device, a request indicating that the user perform a physicalactivity before the one or more preventative actions are disabled (block650). For example, the health platform (e.g., using computing resource224, processor 320, communication interface 370, and/or the like) mayprovide, to the client device, a request indicating that the userperform a physical activity before the one or more preventative actionsare disabled, as described above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include monitoring aperformance of the physical activity by the user (block 660). Forexample, the health platform (e.g., using computing resource 224,processor 320, memory 330, communication interface 370, and/or the like)may monitor a performance of the physical activity by the user, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include determining whetherthe user satisfies the performance of the physical activity based onmonitoring the performance of the physical activity by the user (block670). For example, the health platform (e.g., using computing resource224, processor 320, memory 330, and/or the like) may determine whetherthe user satisfies the performance of the physical activity based onmonitoring the performance of the physical activity by the user, asdescribed above in connection with FIGS. 1A-2.

As further shown in FIG. 6, process 600 may include disabling the one ormore preventative actions when it is determined that the user satisfiesthe performance of the physical activity (block 680). For example, thehealth platform (e.g., using computing resource 224, processor 320,memory 330, communication interface 370, and/or the like) may disablethe one or more preventative actions when it is determined that the usersatisfies the performance of the physical activity, as described abovein connection with FIGS. 1A-2.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or described with regard to any other process described herein.

In some implementations, when performing the one or more preventativeactions, the health platform may cause the client device to disable abrowser associated with the client device, may cause the client deviceto be disabled, may cause the client device to remove a tab from abrowser window associated with the client device, and/or may cause theclient device to block a display of a desktop associated with the clientdevice.

In some implementations, the action may include the user utilizing theclient device to access and browse the Internet for a time period, theuser utilizing the client device to make online purchases that satisfy aprice threshold, the user utilizing the client device to view anindecent web site, and/or the user utilizing the client device to onlinegamble an amount that satisfies a gambling threshold. In someimplementations, the health platform may maintain the one or morepreventative actions when it is determined that the user fails tosatisfy the performance of the physical activity.

In some implementations, when monitoring the performance of the physicalactivity by the user, the health platform may monitor the performance ofthe physical activity via a camera associated with the client device,may monitor the performance of the physical activity via a wearabledevice associated with the user, and/or may monitor the performance ofthe physical activity via user interactions with the client device. Insome implementations, the user may be associated with one or more otherclient devices, and the health platform may perform the one or morepreventative actions, on the one or more other client devices, toprevent the action of the user.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise forms disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, or the like.A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

1. A method, comprising: receiving, by a device and from a clientdevice, behavior data indicating an action of a user of the clientdevice, wherein the action is performed by the user via the clientdevice, wherein the action is associated with online activity of theuser via the client device; processing, by the device, the behaviordata, with a machine learning model, to determine whether the actionsatisfies a behavior threshold, wherein the behavior threshold is one ofa plurality of thresholds associated with an online usage time of theuser via the client device and a usage of an online resource by the uservia the client device, and wherein processing the behavior data with themachine learning model comprises: providing, as input to the machinelearning model, the behavior data, determining, using the machinelearning model, one or more types of behavior associated with theaction, identifying, based on the one or more types of behavior, thebehavior threshold from the plurality of thresholds, and receiving, asoutput from the machine learning model and based on the one or moretypes of behavior, information indicating whether the action satisfiesthe behavior threshold; determining, by the device, one or morepreventative actions to perform to mitigate the action of the user,wherein the one or more preventative actions are determined based on theaction and when the action is determined to satisfy the behaviorthreshold; performing, by the device, the one or more preventativeactions to mitigate the action of the user, wherein the one or morepreventative actions relate to blocking or disabling one or morefunctions of the client device; providing, by the device and to theclient device, a request indicating that the user perform a physicalactivity before the one or more preventative actions are disabled;monitoring, by the device, a performance of the physical activity by theuser, wherein monitoring the performance of the physical activity by theuser includes: monitoring the performance of the physical activity via awearable device associated with the user; determining, by the device,whether the user satisfies the performance of the physical activitybased on monitoring the performance of the physical activity by theuser; and selectively maintaining or disabling, by the device, the oneor more preventative actions based on whether the user satisfies theperformance of the physical activity, wherein the one or morepreventative actions are maintained when the user fails to satisfy theperformance of the physical activity, and wherein the one or morepreventative actions are disabled when the user satisfies theperformance of the physical activity.
 2. The method of claim 1, whereinperforming the one or more preventative actions includes: causing theclient device to be disabled.
 3. The method of claim 1, whereinperforming the one or more preventative actions includes one or more of:causing the client device to disable a browser associated with theclient device; causing the client device to block a display of a browserwindow associated with the client device; causing the client device toblock a display of a particular web site utilized by the user via theclient device; causing the client device to remove a tab from a browserwindow associated with the client device; causing the client device toblock a display of an application utilized by the user via the clientdevice; or causing the client device to block a display of a desktopassociated with the client device.
 4. The method of claim 1, wherein thephysical activity includes one or more of: the user ceasing the actionfor a first time period, the user performing a particular physicalactivity for a second period, or the user performing the particularphysical activity until a particular heart rate of the user is achieved.5. The method of claim 1, wherein monitoring the performance of thephysical activity by the user further includes one or more of:monitoring the performance of the physical activity via a cameraassociated with the client device; or monitoring the performance of thephysical activity via user interactions with the client device.
 6. Themethod of claim 1, further comprising: providing, to the client deviceand prior to receiving the behavior data, an application to be installedon and executed by the client device, and wherein receiving the behaviordata includes: receiving the behavior data via the application.
 7. Themethod of claim 1, wherein the user is associated with one or more otherclient devices and the method further comprises: performing the one ormore preventative actions, on the one or more other client devices, tomitigate the action of the user.
 8. A device, comprising: one or morememories; and one or more processors, communicatively coupled to the oneor more memories, to: receive a machine learning model that has beentrained to determine whether an action of a user satisfies a behaviorthreshold, wherein the behavior threshold is one of a plurality ofthresholds associated with: an online usage time of the user via aclient device, and a usage of an online resource by the user via theclient device; receive, from the client device, behavior data indicatingthe action of the user, wherein the action is performed by the user viathe client device; process the behavior data, with the machine learningmodel, to determine whether the action satisfies the behavior threshold,wherein the one or more processors, when processing the behavior datawith the machine learning model, are to: provide, as input to themachine learning model, the behavior data, determine, using the machinelearning model, one or more types of behavior associated with theaction, identify, based on the one or more types of behavior, thebehavior threshold from the plurality of thresholds, and receive, asoutput from the machine learning model and based on the one or moretypes of behavior, information indicating whether the action satisfiesthe behavior threshold; determine a preventative action to perform toprevent the action of the user, wherein the preventative action isdetermined based on the action and when the action is determined tosatisfy the behavior threshold; perform the preventative action toprevent the action of the user, wherein the preventative action relatesto blocking or disabling one or more functions of the client device;provide, to the client device, a request indicating that the userperform a physical activity before the preventative action is disabled;monitor, via a wearable device associated with the user, a performanceof the physical activity by the user; determine whether the usersatisfies the performance of the physical activity based on monitoringthe performance of the physical activity by the user; and maintain thepreventative action when the user fails to satisfy the performance ofthe physical activity.
 9. The device of claim 8, wherein the one or moreprocessors are further to: disable the preventative action when the usersatisfies the performance of the physical activity.
 10. The device ofclaim 8, wherein the preventative action includes one or more of:causing the client device to disable a browser associated with theclient device, causing the client device to block a display of a browserwindow associated with the client device, causing the client device toblock a display of a particular web site utilized by the user via theclient device, causing the client device to be disabled, causing theclient device to remove a tab from a browser window associated with theclient device, causing the client device to block a display of anapplication utilized by the user via the client device; or causing theclient device to block a display of a desktop associated with the clientdevice.
 11. The device of claim 8, wherein the action includes one ormore of: the user utilizing the client device to access and browse theInternet for a time period, the user utilizing the client device to makeonline purchases that satisfy a price threshold, the user utilizing theclient device to view an indecent web site, or the user utilizing theclient device to online gamble an amount that satisfies a gamblingthreshold.
 12. The device of claim 8, wherein the physical activityincludes one or more of: the user ceasing the action for a first timeperiod, the user performing a particular physical activity for a secondperiod, or the user performing the particular physical activity until aheart rate of the user is achieved.
 13. The device of claim 8, wherein,when monitoring the performance of the physical activity by the user,the one or more processors are to one or more of: monitor theperformance of the physical activity via a camera associated with theclient device; or monitor the performance of the physical activity viauser interactions with the client device.
 14. The device of claim 8,wherein the one or more processors are further to: provide, to theclient device and prior to receiving the behavior data, an applicationto be installed on and executed by the client device, wherein theapplication is to cause the client device to: provide the behavior datato the device, and enable monitoring the performance of the physicalactivity by the user.
 15. A non-transitory computer-readable mediumstoring instructions, the instructions comprising: one or moreinstructions that, when executed by one or more processors of a device,cause the one or more processors to: receive, from a client device,behavior data indicating an action of a user of the client device,wherein the action is performed by the user via the client device;process the behavior data, with a machine learning model, to determinewhether the action satisfies a behavior threshold, wherein the behaviorthreshold is one of a plurality of thresholds associated with: an onlineusage time of the user via the client device, and a usage of an onlineresource by the user via the client device, and wherein the one or moreinstructions, that cause the one or more processors to process thebehavior data with the machine learning model, cause the one or moreprocessors to: provide, as input to the machine learning model, thebehavior data, determine, using the machine learning model, one or moretypes of behavior associated with the action, identify, based on the oneor more types of behavior, the behavior threshold from the plurality ofthresholds, and receive, as output from the machine learning model andbased on the one or more types of behavior associated with the action,information indicating whether the action satisfies the behaviorthreshold; determine one or more preventative actions to perform toprevent the action of the user, wherein the one or more preventativeactions are determined based on the action and when the action isdetermined to satisfy the behavior threshold; perform the one or morepreventative actions to prevent the action of the user, wherein the oneor more preventative actions relate to blocking or disabling one or morefunctions of the client device; provide, to the client device, a requestindicating that the user perform a physical activity before the one ormore preventative actions are disabled; monitor, via a wearable deviceassociated with the user, a performance of the physical activity by theuser; determine whether the user satisfies the performance of thephysical activity based on monitoring the performance of the physicalactivity by the user; and disable the one or more preventative actionswhen it is determined that the user satisfies the performance of thephysical activity.
 16. The non-transitory computer-readable medium ofclaim 15, wherein the one or more instructions, that cause the one ormore processors to perform the one or more preventative actions, causethe one or more processors to one or more of: cause the client device todisable a browser associated with the client device; cause the clientdevice to be disabled; cause the client device to remove a tab from abrowser window associated with the client device; or cause the clientdevice to block a display of a desktop associated with the clientdevice.
 17. The non-transitory computer-readable medium of claim 15,wherein the action includes one or more of: the user utilizing theclient device to access and browse the Internet for a time period, theuser utilizing the client device to make online purchases that satisfy aprice threshold, the user utilizing the client device to view anindecent web site, or the user utilizing the client device to onlinegamble an amount that satisfies a gambling threshold.
 18. Thenon-transitory computer-readable medium of claim 15, wherein theinstructions further comprise: one or more instructions that, whenexecuted by the one or more processors, cause the one or more processorsto: maintain the one or more preventative actions when it is determinedthat the user fails to satisfy the performance of the physical activity.19. The non-transitory computer-readable medium of claim 15, wherein theone or more instructions, that cause the one or more processors tomonitor the performance of the physical activity by the user, cause theone or more processors to one or more of: monitor the performance of thephysical activity via a camera associated with the client device; ormonitor the performance of the physical activity via user interactionswith the client device.
 20. The non-transitory computer-readable mediumof claim 15, wherein the user is associated with one or more otherclient devices and the instructions further comprise: one or moreinstructions that, when executed by the one or more processors, causethe one or more processors to: perform the one or more preventativeactions, on the one or more other client devices, to prevent the actionof the user.